Presents medical diagnosis and control applications of a fuzzy neural inference system that admits both numeric as well as linguistic inputs. Numeric inputs are fuzzified prior to their application to the network; linguistic inputs are presented directly. The network architecture directly embeds fuzzy if-then rules, and connections represent antecedent and consequent fuzzy sets. The novelty of the model lies in its mutual subsethood based activation spread to rule nodes which compute fuzzy inner products. Outputs are computed using volume defuzzification, and gradient descent learning is used to train the network. The model is very economical in terms of the number of rules required to solve difficult problems. Simulation results on two benchmark problems-the Hepatitis data set and the truck backer-upper problem-show that the subsethood based model performs excellently for both applications.